x, 172 pages : illustrations ; 24 cm
  • Introduction: Identification-- Tolerating Ambiguity. Part 1 Extrapolation: Predicting Criminality-- Probabilistic Prediction-- Inferring Conditional Distributions from Random-Sample Data-- Prior Distributional Information-- Predicting High School Graduation. Part 2 The Selection Problem: The Nature of the Problem-- Identification from Censored Samples Alone-- Bounding the Probability of Exiting Homelessness-- Prior Distributional Information-- Identification of Treatment Effects-- Information Linking Outcomes across Treatments-- Predicting High School Graduation If All Families Were Intact. Part 3 The Mixing Problem in Program Evaluation: The Experimental Evaluation of Social Programs-- Variation in Treatment-- The Perry Preschool Project-- Identification of Mixtures Using Only Knowledge of the Marginals-- Restrictions on the Outcome Distribution-- Restrictions on the Treatment Policy-- Identifying Combinations of Assumptions. Part 4 Response-Based Sampling: The odds Ratio and Public Health-- Bounds on Relative and Attributable Risk-- Information on Marginal Distributions-- Sampling from One Response Stratum-- General Binary Stratifications. Part 5 Predicting Individual Behaviour: Revealed Preference Analysis-- How Do Youth Infer the Returns to Schooling?-- Analysis of Intentions Data. Part 6 Simultaneity: "The" Identification Problem in Econometrics-- The Linear Market Model-- Equilibrium in Games-- Simultaneity with Downward-Sloping Demand. Part 7 The Reflection Problem: Endogenous, Contextual, and Correlated Effects-- A Linear Model-- A Pure Endogenous Effects Model-- Inferring the Composition of Reference Groups-- Dynamic Analysis.
  • (source: Nielsen Book Data)9780674442832 20161213
  • Preface Introduction Identification Tolerating Ambiguity 1 Extrapolation 1.1.
  • (source: Nielsen Book Data)9780674442849 20161213
This text provides a language and a set of tools for finding bounds on the predictions that social and behavioural scientists can logically make from non-experimental and experimental data. Economist Charles Manski draws on examples from criminology, demography, epidemiology, social psychology and sociology as well as economics to illustrate this language and to demonstrate the broad usefulness of the tools. There are many traditional ways to present identification problems in econometrics, sociology and psychometrics. Some of these are primarily statistical in nature, using concepts such as flat likelihood functions and non-distinct parameter estimates. Manski's strategy is to divorce identification from purely statistical concepts and to present the logic of identification analysis in ways that are accessible to a wide audience in the social and behavioural sciences. In each case problems are motivated by real examples with real policy importance, the mathematics is kept to a minimum, and the deductions on identifiability are derived providing fresh insights. Manski begins with the conceptual problem of extrapolating predictions from one population to some new population or to the future. He then analyzes the fundamental selection problem that arises whenever a scientist tries to predict the effects of treatments on outcomes. He specifies assumptions and develops his non-parametric methods of bounding predictions. Manski shows how these tools should be used to investigate common problems such as predicting the effect of family structure on children's outcomes and the effect of policing on crime rates. Successive chapters deal with topics such as the use of experiments to evaluate social programmes, the use of case-control sampling by epidemiologists studying the association of risk factors and disease and the use of intentions data by demographers seeking to predict future fertility. The book closes by examining two central identification problems in the analysis of social interactions: the classical simultaneity problem of econometrics and the reflection problem faced in analyses of neighbourhood and contextual effects.
(source: Nielsen Book Data)9780674442832 20161213
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